Name
Abstract | ||
|---|---|---|
To overcome the difficulties related to the computational requirements for solving the optimality systems for optimal control problems on long time horizons, receding horizon techniques provide an important alternative. Rather than finding the optimal solution, a suboptimal control is obtained which achieves the design objective with significantly less computational effort. Moreover, the obtained control can be interpreted as a state feedback control. In this work we continue our analysis of receding horizon strategies, considering the situation when only partial state observations are available. The receding horizon strategy is combined with a state estimator framework. A linear quadratic Gaussian design based on a linearization procedure is proposed and its asymptotic performance is analyzed for systems with nonlinear dynamics. Numerical examples validate the proposed methodology. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1137/S0363012903437988 | SIAM J. Control and Optimization |
Keywords | Field | DocType |
receding horizon control,computational requirement,long time horizon,optimal control problem,receding horizon technique,state estimator framework,incomplete observations,computational effort,suboptimal control,partial state observation,receding horizon strategy,state feedback control | Mathematical optimization,Nonlinear system,Optimal control,Linear-quadratic-Gaussian control,Nonlinear control,Control theory,Horizon,Model predictive control,Mathematics,Linearization,Design objective | Journal |
Volume | Issue | ISSN |
45 | 1 | 0363-0129 |
Citations | PageRank | References |
1 | 0.48 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kazufumi Ito | 1 | 833 | 103.58 |
| Karl Kunisch | 2 | 1370 | 145.58 |